Intrusion detection using classification techniques: a comparative study

Imad Bouteraa, Makhlouf Derdour and Ahmed Ahmim

International Journal of Data Mining, Modelling and Management, 2020, vol. 12, issue 1, 65-86

Abstract: Today's highly connected world suffers from the increase and variety of cyber-attacks. To mitigate those threats, researchers have been continuously exploring different methods for intrusion detection through the last years. In this paper, we study the use of data mining techniques for intrusion detection. The research intends to compare the performances of classification techniques for intrusion detection. To reach the goal, we involve 74 classification techniques in this comparative study. The study shows that no technique outperforms the others in all situations. However, some classification methods lead to promising results and give clues for further combinations.

Keywords: data mining; classification; network security; intrusion detection; KDD99. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=105596 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:12:y:2020:i:1:p:65-86

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().